Identification of spin maneuver aerodynamic nonlinear model by applying ensemble empirical mode decomposition and extended multipoint modeling

Abstract

Identification of the “aircraft aerodynamic model” in some unusual flight conditions such as spin maneuver provides critical information to the flight controller to retake the “dynamic stability” after it has been disturbed by the systematic, natural or environmental oscillations. Hence, a method for identifying the appropriate aerodynamic model in spin maneuvers is presented in this paper. We present an innovative systematic method for aerodynamic modeling of spin maneuvers, which combines the ensemble empirical mode decomposition technique and extended multipoint modeling approach, using flight data. In ensemble empirical mode decomposition, in addition to having all the empirical mode decomposition features, the original signal is collected with the white noise, and by using its statistical characteristics, the ensemble empirical mode decomposition solves the mode mixing problem. By applying the ensemble empirical mode decomposition to the flight parameter data, their intrinsic mode frequencies are extracted and are used as inputs to the extended multipoint modeling model. The extended multipoint modeling structure includes some parameters describing the distribution of aerodynamic forces and moments along each of the aircraft components. Moreover, this method allows coupling between the forces and moments. Unlike conventional methods, which consider the average forces obtained by plane surfaces relative to the center of mass, in the extended multipoint modeling technique, the force generated by each plane of the aircraft is allowed to appear independently in the motion equations. For identifying the aerodynamic model with extended multipoint modeling structure, the equation error method is used with a maximum likelihood optimizer inside. The obtained algorithm has been applied to two sets of spin maneuver flight data which were recorded in actual spin flight. The results demonstrate that the proposed method is able to reproduce the aerodynamic forces and moments for the second spin flight inputs with high accuracy by using a model which is derived from the first spin data identification.

The Effects of Microburst Nonlinear Varying Loads on Aircraft Using a Multi-Point Approach

“…Microburst wind loads act on the aircraft according to equations (35) to (37) for yaw, pitch, and roll moments:…”

A review and historical development of analytical techniques to predict aircraft spin and recovery characteristics

“…Although system identification has been used since late 1960s to identify aircraft models in the normal flight regime, its application to identify spin models is relatively recent (Klein and Morelli, 2016). Several modeling approaches, including neural networks (Cho and Nagati, 1995), local model networks (Dias, 2013), locally weighted regression (Bunge and Kroo, 2018) and empirical mode decomposition (Mokhtari and Sabzehparvar, 2019) have been used to identify aircraft spin models. As the flight data is collected from the actual spinning aircraft, there are no scale errors pertaining to dynamic similitude requirements.…”

The Effects of Microburst Varying Loads on Aircraft Using a Multi-Point Approach

“…A multipoint model was used by Mokhtari et al to define an aerodynamic model of the aircraft, with forces and moments coupled along each component in the spinning condition. They used two sets of real flight data to estimate the nonlinear aerodynamic functions and produced results with acceptable accuracy [35].…”

“…[(𝑑𝜎𝐹 ̑+ 𝑑𝜓𝑁 ̑)}. 𝑑𝜂𝑆 ]Microburst wind loads act on the aircraft according to equations(35) to(37) for yaw, pitch, and roll moments:…”